A colleague of mine is arguing that we should look into using some structural time series tools for improving our demand forecasting (I work in retail). I am a little bit skeptical.
I don't see any benefit to being able to model structural changes in our use case (relatively short series - 1 ~ 2 years of weekly sales data).
But then it struck me: Does modeling structural changes help at all with forecasting?
If there are structural changes to the series, then we are outside of statistical forecasting territory and in need of a domain expert to explain what happened.
There's no point in trying to model the structural change as it won't help with any algorithmic improvement in forecasting accuracy.
At most we can detect the structural change with the purpose of discarding the data prior to the change since it is no longer relevant - but other than that I don't see any purpose in trying to model structural changes.
Is this line of reasoning correct?